Impact of social distancing on disease transmission risk in the context of a pandemic

Changes in pedestrian dynamics caused by social distancing policies place new demands on pedestrian motion modeling during the pandemic. This study summarizes pedestrian movement characteristics during the pandemic, based on which, the traditional floor-field cellular automata model was improved by introducing two floor fields related to pedestrian density to simulate social distancing in crowded places. Especially, the cumulative density field guides pedestrians in route selection, thereby compensating for the limitation of the previous models in which only local repulsion was considered. By selecting an appropriate combination of parameters, the desired social distancing behavior can be observed. Then, the rationality of our model is verified by the fundamental diagram. Moreover, to assess the influences of social distancing on the risk of disease transmission, we considered both person-person transmission and environment-person transmission. The simulation results show that although social distancing is effective in preventing interpersonal transmission, an increase in environmental transmission may somewhat offset this effect. We also examined the influence of individual motion heterogeneity on infection spread and found that the containment was the best when only patients complied with the social distancing restriction. The trade-off between safety and efficiency associated with social distancing was also initially explored in this study.